Update demo/MMSegmentation_Tutorial.ipynb

ca1dc717 · Miao Zheng · GitHub · 2b4dbcfb · ca1dc717
Unverified Commit ca1dc717 authored 2 years ago by Miao Zheng Committed by GitHub 2 years ago
--- a/demo/MMSegmentation_Tutorial.ipynb
+++ b/demo/MMSegmentation_Tutorial.ipynb
@@ -67,7 +67,7 @@
   "outputs": [],
   "source": [
    "# Install PyTorch\n",
-    "!conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 - c pytorch\n",
+    "!conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch\n",
    "# Install mim\n",
    "!pip install -U openmim\n",
    "# Install mmengine\n",

 %% Cell type:markdown id: tags:

 <a href="https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/dev-1.x/demo/MMSegmentation_Tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

 %% Cell type:markdown id: tags:

 # MMSegmentation Tutorial
 Welcome to MMSegmentation!

 In this tutorial, we demo
 * How to do inference with MMSeg trained weight
 * How to train on your own dataset and visualize the results.

 %% Cell type:markdown id: tags:

 ## Install MMSegmentation
 This step may take several minutes.

 We use PyTorch 1.12 and CUDA 11.3 for this tutorial. You may install other versions by change the version number in pip install command.

 %% Cell type:code id: tags:

 ``` python
 # Check nvcc version
 !nvcc -V
 # Check GCC version
 !gcc --version
 ```

 %% Cell type:code id: tags:

 ``` python
 # Install PyTorch
-!conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 - c pytorch
+!conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
 # Install mim
 !pip install -U openmim
 # Install mmengine
 !mim install mmengine
 # Install MMCV
 !mim install 'mmcv >= 2.0.0rc1'
 ```

 %% Cell type:code id: tags:

 ``` python
 !rm -rf mmsegmentation
 !git clone -b dev-1.x https://github.com/open-mmlab/mmsegmentation.git
 %cd mmsegmentation
 !pip install -e .
 ```

 %% Cell type:code id: tags:

 ``` python
 # Check Pytorch installation
 import torch, torchvision
 print(torch.__version__, torch.cuda.is_available())

 # Check MMSegmentation installation
 import mmseg
 print(mmseg.__version__)
 ```

 %% Cell type:markdown id: tags:

 ## Finetune a semantic segmentation model on a new dataset

 To finetune on a customized dataset, the following steps are necessary.
 1. Add a new dataset class.
 2. Create a config file accordingly.
 3. Perform training and evaluation.

 %% Cell type:markdown id: tags:

 ### Add a new dataset

 Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure.

 In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#customize-datasets-by-reorganizing-data) for details about dataset reorganization.

 We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels.
 In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object.

 %% Cell type:code id: tags:

 ``` python
 # download and unzip
 !wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O stanford_background.tar.gz
 !tar xf stanford_background.tar.gz
 ```

 %% Cell type:code id: tags:

 ``` python
 # Let's take a look at the dataset
 import mmcv
 import mmengine
 import matplotlib.pyplot as plt


 img = mmcv.imread('iccv09Data/images/6000124.jpg')
 plt.figure(figsize=(8, 6))
 plt.imshow(mmcv.bgr2rgb(img))
 plt.show()
 ```

 %% Cell type:markdown id: tags:

 We need to convert the annotation into semantic map format as an image.

 %% Cell type:code id: tags:

 ``` python
 # define dataset root and directory for images and annotations
 data_root = 'iccv09Data'
 img_dir = 'images'
 ann_dir = 'labels'
 # define class and palette for better visualization
 classes = ('sky', 'tree', 'road', 'grass', 'water', 'bldg', 'mntn', 'fg obj')
 palette = [[128, 128, 128], [129, 127, 38], [120, 69, 125], [53, 125, 34],
           [0, 11, 123], [118, 20, 12], [122, 81, 25], [241, 134, 51]]
 ```

 %% Cell type:code id: tags:

 ``` python
 import os.path as osp
 import numpy as np
 from PIL import Image

 # convert dataset annotation to semantic segmentation map
 for file in mmengine.scandir(osp.join(data_root, ann_dir), suffix='.regions.txt'):
  seg_map = np.loadtxt(osp.join(data_root, ann_dir, file)).astype(np.uint8)
  seg_img = Image.fromarray(seg_map).convert('P')
  seg_img.putpalette(np.array(palette, dtype=np.uint8))
  seg_img.save(osp.join(data_root, ann_dir, file.replace('.regions.txt',
                                                         '.png')))
 ```

 %% Cell type:code id: tags:

 ``` python
 # Let's take a look at the segmentation map we got
 import matplotlib.patches as mpatches
 img = Image.open('iccv09Data/labels/6000124.png')
 plt.figure(figsize=(8, 6))
 im = plt.imshow(np.array(img.convert('RGB')))

 # create a patch (proxy artist) for every color
 patches = [mpatches.Patch(color=np.array(palette[i])/255.,
                          label=classes[i]) for i in range(8)]
 # put those patched as legend-handles into the legend
 plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,
           fontsize='large')

 plt.show()
 ```

 %% Cell type:code id: tags:

 ``` python
 # split train/val set randomly
 split_dir = 'splits'
 mmengine.mkdir_or_exist(osp.join(data_root, split_dir))
 filename_list = [osp.splitext(filename)[0] for filename in mmengine.scandir(
    osp.join(data_root, ann_dir), suffix='.png')]
 with open(osp.join(data_root, split_dir, 'train.txt'), 'w') as f:
  # select first 4/5 as train set
  train_length = int(len(filename_list)*4/5)
  f.writelines(line + '\n' for line in filename_list[:train_length])
 with open(osp.join(data_root, split_dir, 'val.txt'), 'w') as f:
  # select last 1/5 as train set
  f.writelines(line + '\n' for line in filename_list[train_length:])
 ```

 %% Cell type:markdown id: tags:

 After downloading the data, we need to implement `load_annotations` function in the new dataset class `StanfordBackgroundDataset`.

 %% Cell type:code id: tags:

 ``` python
 from mmseg.registry import DATASETS
 from mmseg.datasets import BaseSegDataset


 @DATASETS.register_module()
 class StanfordBackgroundDataset(BaseSegDataset):
  METAINFO = dict(classes = classes, palette = palette)
  def __init__(self, **kwargs):
    super().__init__(img_suffix='.jpg', seg_map_suffix='.png', **kwargs)

 ```

 %% Cell type:markdown id: tags:

 ### Create a config file
 In the next step, we need to modify the config for the training. To accelerate the process, we finetune the model from trained weights.

 %% Cell type:code id: tags:

 ``` python
 # Download config and checkpoint files
 !mim download mmsegmentation --config pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 --dest .
 ```

 %% Cell type:code id: tags:

 ``` python
 from mmengine import Config
 cfg = Config.fromfile('configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
 print(f'Config:\n{cfg.pretty_text}')
 ```

 %% Cell type:markdown id: tags:

 Since the given config is used to train PSPNet on the cityscapes dataset, we need to modify it accordingly for our new dataset.

 %% Cell type:code id: tags:

 ``` python
 # Since we use only one GPU, BN is used instead of SyncBN
 cfg.norm_cfg = dict(type='BN', requires_grad=True)
 cfg.crop_size = (256, 256)
 cfg.model.data_preprocessor.size = cfg.crop_size
 cfg.model.backbone.norm_cfg = cfg.norm_cfg
 cfg.model.decode_head.norm_cfg = cfg.norm_cfg
 cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg
 # modify num classes of the model in decode/auxiliary head
 cfg.model.decode_head.num_classes = 8
 cfg.model.auxiliary_head.num_classes = 8

 # Modify dataset type and path
 cfg.dataset_type = 'StanfordBackgroundDataset'
 cfg.data_root = data_root

 cfg.train_dataloader.batch_size = 8

 cfg.train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='RandomResize', scale=(320, 240), ratio_range=(0.5, 2.0), keep_ratio=True),
    dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackSegInputs')
 ]

 cfg.test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(320, 240), keep_ratio=True),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
 ]


 cfg.train_dataloader.dataset.type = cfg.dataset_type
 cfg.train_dataloader.dataset.data_root = cfg.data_root
 cfg.train_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
 cfg.train_dataloader.dataset.pipeline = cfg.train_pipeline
 cfg.train_dataloader.dataset.ann_file = 'splits/train.txt'

 cfg.val_dataloader.dataset.type = cfg.dataset_type
 cfg.val_dataloader.dataset.data_root = cfg.data_root
 cfg.val_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
 cfg.val_dataloader.dataset.pipeline = cfg.test_pipeline
 cfg.val_dataloader.dataset.ann_file = 'splits/val.txt'

 cfg.test_dataloader = cfg.val_dataloader


 # Load the pretrained weights
 cfg.load_from = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

 # Set up working dir to save files and logs.
 cfg.work_dir = './work_dirs/tutorial'

 cfg.train_cfg.max_iters = 200
 cfg.train_cfg.val_interval = 200
 cfg.default_hooks.logger.interval = 10
 cfg.default_hooks.checkpoint.interval = 200

 # Set seed to facilitate reproducing the result
 cfg['randomness'] = dict(seed=0)

 # Let's have a look at the final config used for training
 print(f'Config:\n{cfg.pretty_text}')
 ```

 %% Cell type:markdown id: tags:

 ### Train and Evaluation

 %% Cell type:code id: tags:

 ``` python
 from mmengine.runner import Runner
 from mmseg.utils import register_all_modules

 # register all modules in mmseg into the registries
 # do not init the default scope here because it will be init in the runner
 register_all_modules(init_default_scope=False)
 runner = Runner.from_cfg(cfg)
 ```

 %% Cell type:code id: tags:

 ``` python
 # start training
 runner.train()
 ```

 %% Cell type:markdown id: tags:

 Inference with trained model

 %% Cell type:code id: tags:

 ``` python
 from mmseg.apis import init_model, inference_model, show_result_pyplot

 # Init the model from the config and the checkpoint
 checkpoint_path = './work_dirs/tutorial/iter_200.pth'
 model = init_model(cfg, checkpoint_path, 'cuda:0')

 img = mmcv.imread('iccv09Data/images/6000124.jpg')
 result = inference_model(model, img)
 plt.figure(figsize=(8, 6))
 vis_result = show_result_pyplot(model, img, result)
 plt.imshow(mmcv.bgr2rgb(vis_result))
 ```